Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
30,912
datasets available to search
ShareScore release 0.7.1
Dataset results
30,912 results for “profiles”
Arctic fish biomarker profiles from Beaufort Sea coastal lagoons, 2017–2022
Fish sampling occurred in three regions across the Beaufort Sea coast: Elson Lagoon in Utqiaġvik, Stefansson Sound in Prudhoe Bay, and Kaktovik and Jago lagoons in Barter Island (city of Kaktovik). Arctic fishes were collected to determine trophic niche overlap by determining stomach contents, bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) profiles. Fishes were collected in each of the three regions during the open water season in August 2021 and 2022, with supplemental samples collected in 2017 – 2019. The target fish species included three diadromous species: Arctic Cisco (Coregonus autumnalis), Least Cisco (Coregonus sardinella), and Dolly Varden (Salvelinus malma), and three marine fish species: Polar Cod (Boreogadus saida), Saffron Cod (Eleginus gracilis), and Fourhorn Sculpin (Myoxocephalus quadricornis). Up to ten individuals per species per region were sampled, but not all species could be collected in all regions. Stomach contents were reported as the total number of individuals per prey category for the following categories: Amphipoda, Polychaeta, Harpacticoidea, Saduria entomon, Nemertea, Priapulida, Cumacea, Mysidacea, Calanoidea, Larval fish, Chironomida, Insecta, Misc. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 35 fatty acids: C8:0, C10:0, C11:0, C12:0, C13:0, C14:0, C14:1n5, C15:0, C15:1, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:1n7, C18:2n6 trans, C18:2n6 cis, C18:3n3, C18:3n6, C20:0, C20:1n9, C20:2n6, C21:0, C20:3n6, C22:0, C20:4n6, C20:3n3, C20:5n3, C22:1n9, C22:2n6, C23:0, C24:0, C22:6n3, C24:1n9. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe), Lysine (Lys).
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
CO2 Profile at Harvard Forest HEM and LPH Towers since 2009
Carbon dioxide profile measurements are designed to supplement eddy flux measurements in calculating the carbon exchange of a forest. In the case of the Harvard Forest flux towers, the change in CO2 storage between the ground and the height of the eddy flux system during each half hour must be added to the eddy flux measured above the forest to determine the total carbon exchange. The profile measurements are also necessary in order to calculate CO2 movement by advection, or horizontal air transport. The amount of CO2 removed from a particular site by advection is the product of the total CO2 within the air a given range of height above the ground, and the average velocity of airflow parallel to the ground surface within that height range.
Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma
Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for
Time-series of high-frequency profiles of fluorescence-based phytoplankton spectral groups in Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2014-2025
Depth profiles of fluorescence-based phytoplankton biomass were sampled using a bbe Moldaenke FluoroProbe (Schwentinental, Germany) during 2014 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of depth profiles of fluorescence-based phytoplankton biomass measured at the deepest site of each reservoir adjacent to the dam, except in Falling Creek Reservoir, where depth profiles were also taken at four upstream sites ranging from the riverine to the lacustrine zone during 2016-2019 and 2024-2025. Casts were taken approximately weekly from May-October and monthly from November-April. Casts were collected at Beaverdam and Falling Creek Reservoirs during all years (2014-2025); casts were collected at Carvins Cove Reservoir during 2014-2016, 2018-2023, and 2025; casts were collected at Spring Hollow Reservoir during 2014-2016 and 2019; and casts were collected at Gatewood Reservoir in 2015-2016. A sensor maintenance log and quality assurance/quality control analysis script accompanies the data package.
Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The data set consists of the vertical profiles of the atmospheric variables measured using radiosondes (i-Met) during the Antarctic Circumnavigation Expedition from November 2016 to April 2017. The data include the raw variables measured directly by the radiosondes and derived parameters: altitude (km), air pressure (mb), air temperature (ºC), relative humidity (%), frostpoint (ºC), potential temperature (ºK), water vapour mixing ratio (ppmv), total column water (mm w.e.), wind speed (m/s) and wind direction (deg).</p> <p><strong>Dataset contents</strong></p> <ul> <li>aceNNN_yyyymmdd, directory <ul> <li>aceNNN_yyyymmdd.csv, data file, comma-separated values</li> <li>aceNNN_yyyymmdd.kml, metadata, XML</li> <li>aceNNN_yyyymmdd.raw, data file, raw, ASCII DOS</li> <li>aceNNN_yyyymmdd.raw_config, metadata, XML</li> <li>aceNNN.de1, metadata, ASCII text format</li> <li>aceNNNflt.dat, data file, ASCII text format</li> <li>aceNNNpre.dat, data file, ASCII text format</li> </ul> </li> <li>plots, directory <ul> <li>Sounding_ACENNN.png, metadata, portable network graphics</li> </ul> </li> <li>data_file_header_csv.txt, metadata, text format</li> <li>data_file_header_dat.txt, metadata, text format</li> <li>data_file_header_launches.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>overview_radiosonde_launches.csv, metadata, comma-separated value</li> </ul> <p>where NNN is the launch number yyyy is the year, mm is the month and dd is the day. Dates are in UTC.</p> <p>json files make up a Frictionless Data package.</p> <p><strong>Dataset citation</strong></p> <p>Please cite this dataset as:</p> <p>Gorodetskaya, I.V., Thurnherr, I., Tsukernik, M., Graf, P., Aemisegger, F., Wernli, H. and Ralph, F.M. (2021). Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4382460</p>
MethylDetectR - A Translational Tool for Methylation-Based Health Profiling
<p><strong>** CORRECTION (2025-05-29): Please note that the script <a href="https://zenodo.org/api/records/15548022/draft/files/Script_For_User_To_Generate_Scores.R/content" target="_blank" rel="noopener noreferrer">Script_For_User_To_Generate_Scores.R</a> had an error whereby single-CpG EpiScores were producing the same score for all samples. This has been corrected in the newest version. </strong><br><br>This dataset includes reproducible code for the two applications related to the 'MethylDetectR' software. These .R files are included as 'MethylDetectR - Calculate Your Scores.R' and 'MethylDetectR.R'. An example DNAm file and SexAgeinfo file for upload to 'MethylDetectR - Calculate Your Scores' are included. These are 'DNAm_File_Example.rds' and 'SexAgeinfo_example.csv' respectively. An example output file from this application/for upload to 'MethylDetectR' is included as 'MethylDetectR - Test For Upload.csv'. An example and optional input file for case/control data is also available as 'MethylDetectR_Case_Control_Example.csv'. </p> <p>Furthermore, a script for the user to generate their own DNAm-based estimated values for human traits is included as 'Script_For_User_To_Generate_Scores.R'. A necessary associated file as 'Predictors_Shiny_By_Groups.csv' is also present for the script to run. We have also included separate necessary files to generate the chronological age predictor from Bernabeu <em>et al.</em> in our most recent versions of MethylDetectR. </p> <p>Lastly, an additional file called 'Truncate_to_these_CpGs.csv' is available which allows users to subset their methylation file to those CpG sites used in the 'MethylDetectR - Calculate Your Scores' application. This may substantially reduce the size of the methylation file for upload as well as its upload time. </p>
TCOM-CFC11 : TOMCAT CTM and Occultation Mesurement based daily zonal mean CFC-11 stratospheric profiles constructed using machine leaning (2000-2023)
<h3>TOMCAT CTM and Occultation measurement-based Stratospheric CFC11 (TCOM-CFC11) profile data data set </h3> <h3>Sandip S. Dhomse </h3> <p>School of Earth and Envio, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <h3>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC11 (CFCl3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-11 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</h3> <h3>Dataset also includes two files containing daily mean zonal mean CFC11 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</h3> <h3>zmcfc11_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</h3> <h3>zmcfc11_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</h3> <h3>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</h3> <h3> </h3> <h3> </h3>
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Data for "Profiling the transcriptomic age of single-cells in humans"
<p>This is a supplementary data for the article titled "Profiling transcriptomic age of human single-cells". Data created in this project is shared here for the scientific community. </p> <p>Here we used available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors, for developing cell-type-specific single-cell transcriptomic clocks and predicting the age of human blood cells. We also applied our clocks to different external datasets and evaluated the age of single cells originated from COVID-19 patients and human embryos.</p> <p>For the description of the content of the dataset see the ReadMe file.</p>
Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Data set of the manuscript titled: Follicular Immune Landscaping Reveals a distinct profile of FOXP3hi CD4+ T cells in Treated compared to Untreated HIV
<p>Multiplex imaging data were collected using a scanning confocal system (STELARIS, Leica) and proccessed with the Imaris and Fiji imaging programs. csv files incuding the position identifiers and intensities for each fluorochrome used were generated and data were further analysed using the FlowJo10 program. Neighboring analysis was performed using the G function and mean of minimum distances of relevant cell type pairs. </p>
Bidirectional and Unidirectional Charging Profiles of Electric Vehicles
<p>This dataset contains bidirectional and unidirectional charging profiles of Electric Vehicles (EVs) measured in laboratory environment at the Smart Grid Technology Lab of ie³ institute at TU Dortmund University. The dataset not only considers charging power and current but also harmonics/interharmonics emission of EV charging in both static and dynamic scenarios. Thus, it provides a solid foundation for the development of advanced EV charging algorithms and model validation. Raw data are available in csv format from the file <em>dataset_raw.zip</em> and a selection of merged measurements is provided in the file <em>dataset_merged.zip</em>.</p> <p>The following commercially available EV models are considered:</p> <ul> <li>Opel Corsa-e (2020)</li> <li>Fiat 500e (2022)</li> <li>Honda-e Advance (bidirectional, 2020)</li> <li>Nissan Leaf (bidirectional, 2020)</li> <li>VW ID.4 (2020)</li> <li>Hyundai Ioniq 5 (2021)</li> <li>Mitsubishi Eclipse Cross PHEV (bidirectional, 2022)</li> <li>Tesla Model Y SR (2022)</li> </ul> <p>The dataset is part of the deliverable D8.1 of DriVe2X project and is accompanied by a report including a description about data acquisition and measurement setup. The report is available from the project website's resources section. A more in-depth description of the tests and exemplary analysis is currently being prepared for publication.</p> <p><strong>References</strong></p> <ul> <li>DriVe2X project website: <a href="https://drive2x.eu/">Link</a></li> <li>CORDIS website: <a href="https://cordis.europa.eu/project/id/101056934">Link</a></li> <li>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></li> <li>Smart Grid Technology Lab: <a href="http://sgtl.et.tu-dortmund.de/">Link</a></li> </ul>
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.